NLSIT: A Non-Local Stereo Interaction Transformer for Stereo Image Super-Resolution

Abstract

In recent years, although Transformer has been introduced into stereo image super-resolution and accomplished great advances, the long-range complementary information in stereo images hasn’t been fully utilized. In view of beneficial non-local prior knowledge in both intra-view and cross-view, we propose an efficient Non-Local Stereo Interaction Transformer (NLSIT) to exploit long-range complementary prior. NLSIT mainly consists of non-local channel interaction block (NLCIB) and non-local spatial interaction block (NLSIB). NLCIB extracts channel correlations across the two views by a channel interaction attention mechanism with linear complexity and NLSIB is devised to capture non-local spatial dependencies with locality sensitive hashing (LSH) enforcing sparsity and relevancy of the attention range. Extensive experiments demonstrate that our NLSIT outperforms most SOTA methods on several popular stereo image datasets with much fewer parameters, showing the effectiveness of the proposed framework.